We envision to keep fostering on continuous integration and development of highly reproducible workflows.
The snakemake html report can be viewed using any compartible browser such as chrome to explore more on the workflow and the associated statistics. You will be able to close the left bar to get a better view of the dispaly.
run accessions are available via
the SRA
database.Demo: Downloading metadata associated with the bushmeat microbiome bioproject number PRJNA477349 Note that the SRA filename for metadata is automatically named SraRunTable.txt (not CSV).
Screen shot of SRA Run Selector for metadata associated with the NCBI-SRA bioproject number PRJNA477349
NA in location column.RDS or RData format
will preserve a compressed file but here we save a tidy file as
CSV.workflow/scripts/tidy_metadata.At this stage you will be able to select and decide what to include in the downstream analyses.
Bar chart depicting variables against read counts.
leaflet R
package can do a great job in dropping a pin on the corresponding
coordinate.Static image from the interactive HTML map converted using the html2image package.
R is a free software for statistical computing, data analysis, and graphics[4]. We need to install R application on a personal computer to process the R programming language. You can download and install R using these steps:
RStudio is a free program that integrates with R as an IDE (Integrated Development Environment) to implement most of the analytical functionalities[5]. For effective analysis, we must install R before installing RStudio. We will intensively use RStudio IDE to give us a user interface. We are interested in RStudio Desktop, which is the open-source regular desktop application. You can install it like this:
Screen shot of RStudio User Interface
There several tools out there that can help in preprocessing raw read. Listed below are some of most common tools used in understanding the characteristics of the read and their quality scores. Click on the hyperlinked tool to learn more how to install it.
Note that the links for each tool may be outdated. Make sure to check for latest instructions online.
Read sequencing data may be obtained from different sources. The most common ones include:
insilico data is used for testing software before
using real data.insilico data can be challenging but not can
provide a starting data for testing some pipelines.insilico sequencing data. Most suitable for testing
metagenomics analysis tools.seqkit sample
function[11].This example extract 1% of the reads in only two sample (SRR10245277 & SRR10245278)
mkdir -p data
for i in {77..78}
do
cat SRR102452$i\_R1.fastq \
| seqkit sample -p 0.01 \
| seqkit shuffle -o data/SRR102452$i\_R1_sub.fastq \
| cat SRR102452$i\_R2.fastq \
| seqkit sample -p 0.01 \
| seqkit shuffle -o data/SRR102452$i\_R2_sub.fastq
done
The mapping files are required to direct the pipeline where to look for the files containing the sequencing data.
mothur and
QIIME2 pipelines is slightly different.# A tibble: 2 × 3
sample_id forward reverse
<chr> <chr> <chr>
1 SRR10245277 SRR10245277_1.fastq SRR10245277_2.fastq
2 SRR10245278 SRR10245278_1.fastq SRR10245278_2.fastq
# A tibble: 2 × 2
group var1
<chr> <chr>
1 SRR10245277 Serengeti
2 SRR10245278 Serengeti
.
├── LICENSE
├── README.md
├── Rplots.pdf
├── bioinformatics.png
├── config
│ └── config.yaml
├── css
│ └── styles.css
├── dags
│ ├── dag.png
│ ├── dag.svg
│ ├── rulegraph.png
│ └── rulegraph.svg
├── data
│ ├── design.tsv
│ ├── metadata
│ └── reads
├── images
│ ├── RStudioIDE.png
│ ├── bioinformatics.png
│ ├── bkgd1.png
│ ├── bkgd2.png
│ ├── cicd.png
│ ├── imap-part1.png
│ ├── metadata.png
│ ├── planning.png
│ ├── project_tree.txt
│ ├── sample_gps.html
│ ├── sample_gps.png
│ ├── sample_gps_files
│ ├── smkreport
│ ├── sra_run_selector.png
│ ├── variable_freq.png
│ └── variable_freq.svg
├── index.Rmd
├── index.html
├── library
│ ├── apa.csl
│ └── references.bib
├── microbiome-analysis.Rproj
├── report.html
├── resources
├── results
│ ├── read_size_asce.csv
│ ├── read_size_desc.csv
│ └── stats1
└── workflow
├── Snakefile
├── envs
├── notebooks
├── reports
├── rules
├── schemas
└── scripts
20 directories, 34 files